The popularity of digital images is rapidly increasing due to improving digital imaging technologies and convenient availability facilitated by the Internet. More and more digital images are becoming available every day. The images are kept in image databases, and retrieval systems provide an efficient mechanism for users to navigate through the growing numbers of images in the image databases.
Traditional image retrieval systems allow users to retrieve images in one of two ways: (1) keyword-based image retrieval or (2) content-based image retrieval. Keyword-based image retrieval finds images by matching keywords from a user query to keywords that have been added to the images. Content-based image retrieval (CBIR) finds images that have low-level image features similar to those of an example image, such as color histogram, texture, shape, and so forth. However, CBIR has a drawback in that searches may return entirely irrelevant images that just happen to possess similar features. Since content-based image retrieval has a low performance level, keyword-based image search is more preferable.
To facilitate keyword-based image retrieval, the images (or generally, multimedia objects) must first be labeled with one or more keywords. Labeling semantic content of images, or multimedia objects, with a set of keywords is a process known as image (or multimedia) annotation. Annotated images can be found using keyword-based search, while un-annotated image cannot.
Currently, most of the image database systems employ manual annotation, where users add descriptive keywords when the images are loaded, registered, or browsed. Manual annotation of image content is accurate because keywords are selected based on human perception of the semantic content of images. Unfortunately, manual annotation is obviously a labor intensive and tedious process. In fact, it may also introduce errors due to absent-minded and/or subjective users. Therefore, people are reluctant to use it.
To overcome the problems of manual annotation, automatic image annotation techniques have been proposed. One research team, for example, attempted to use image recognition techniques to automatically select appropriate descriptive keywords (within a predefined set) for each image. See, Ono, A et al., “A Fexible Content-Based Image Retrieval System with Combined Scene Description Keyword”, Proceedings of IEEE Int. Conf. on Multimedia Computing and Systems, pp. 201-208, 1996. However, automatic image annotation has only been tested with very limited keywords and image models. It is not realistic to handle a wide range of image models and concepts. Moreover, since image recognition technique is admittedly at a low performance level, people cannot trust those keywords obtained automatically without their confirmation/verification.
Accordingly, there is a need for a new technique for annotating images, or other multimedia objects.